| Zugriffsnummer | 50849 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Data-efficient Bayesian learning for radial dynamic MR reconstruction |
| Autor(in); Institution |
Brahma, Sherine; 8.1, Biomedizinische Magnetresonanz, PTB-Berlin
Martin, Jörg; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
|
| Quelle/Jahr | Medical Physics: 50 (2023), 11, 6955 - 6977 |
| ISSN | 0094-2405 (print) ; 2473-4209 (online) |
| DOI | |
| Verlag | Hoboken, NJ: Wiley |
| Freie Schlagworte | cine MRI ; deep learning ; uncertainty quantification |
| Zusammenfassung | Using an XT-YT U-Net,we were able to quantify uncertainties of a physics-informed NN for a high-dimensional and computationally demanding 2D multi-coil dynamic MR imaging problem. In addition to improving the image quality, embedding the acquisition model in the network architecture decreased the reconstruction uncertainties as well as quantitatively improved the UQ. The UQ provides additional information to assess the performance of different network approaches. |
| Kostenfreier Zugang | Open Access Hybrid |
| Rechteinformation | CC BY 4.0 ; Creative Commons Attribution 4.0 License |
| Themenbereich der Metrologie | Metrologie in der Medizin |